/
commands.py
929 lines (743 loc) · 32.6 KB
/
commands.py
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"""
Functions implementing the main command-line subcommands.
"""
import csv
import os
import os.path
import sys
import screed
from .compare import compare_all_pairs, compare_serial_containment
from . import MinHash, load_sbt_index, create_sbt_index
from . import signature as sig
from . import sourmash_args
from .logging import notify, error, print_results, set_quiet
from .sbtmh import SearchMinHashesFindBest, SigLeaf
from .sourmash_args import DEFAULT_LOAD_K, FileOutput
DEFAULT_N = 500
WATERMARK_SIZE = 10000
from .command_compute import compute
def compare(args):
"Compare multiple signature files and create a distance matrix."
import numpy
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
inp_files = list(args.signatures)
if args.from_file:
more_files = sourmash_args.load_file_list_of_signatures(args.from_file)
inp_files.extend(more_files)
progress = sourmash_args.SignatureLoadingProgress()
# load in the various signatures
siglist = []
ksizes = set()
moltypes = set()
for filename in inp_files:
notify("loading '{}'", filename, end='\r')
loaded = sourmash_args.load_file_as_signatures(filename,
ksize=args.ksize,
select_moltype=moltype,
yield_all_files=args.force,
progress=progress)
loaded = list(loaded)
if not loaded:
notify('\nwarning: no signatures loaded at given ksize/molecule type from {}', filename)
siglist.extend(loaded)
# track ksizes/moltypes
for s in loaded:
ksizes.add(s.minhash.ksize)
moltypes.add(sourmash_args.get_moltype(s))
# error out while loading if we have more than one ksize/moltype
if len(ksizes) > 1 or len(moltypes) > 1:
break
if not siglist:
error('no signatures found! exiting.')
sys.exit(-1)
# check ksizes and type
if len(ksizes) > 1:
error('multiple k-mer sizes loaded; please specify one with -k.')
ksizes = sorted(ksizes)
error('(saw k-mer sizes {})'.format(', '.join(map(str, ksizes))))
sys.exit(-1)
if len(moltypes) > 1:
error('multiple molecule types loaded; please specify --dna, --protein')
sys.exit(-1)
notify(' '*79, end='\r')
notify('loaded {} signatures total.'.format(len(siglist)))
# check to make sure they're potentially compatible - either using
# max_hash/scaled, or not.
scaled_sigs = [s.minhash.max_hash for s in siglist]
is_scaled = all(scaled_sigs)
is_scaled_2 = any(scaled_sigs)
# complain if it's not all one or the other
if is_scaled != is_scaled_2:
error('cannot mix scaled signatures with bounded signatures')
sys.exit(-1)
# complain if --containment and not is_scaled
if args.containment and not is_scaled:
error('must use scaled signatures with --containment option')
sys.exit(-1)
# notify about implicit --ignore-abundance:
if args.containment:
track_abundances = any(( s.minhash.track_abundance for s in siglist ))
if track_abundances:
notify('NOTE: --containment means signature abundances are flattened.')
# if using --scaled, downsample appropriately
printed_scaled_msg = False
if is_scaled:
max_scaled = max(s.minhash.scaled for s in siglist)
for s in siglist:
if s.minhash.scaled != max_scaled:
if not printed_scaled_msg:
notify('downsampling to scaled value of {}'.format(max_scaled))
printed_scaled_msg = True
s.minhash = s.minhash.downsample(scaled=max_scaled)
if len(siglist) == 0:
error('no signatures!')
sys.exit(-1)
notify('')
# build the distance matrix
numpy.set_printoptions(precision=3, suppress=True)
# do all-by-all calculation
labeltext = [item.name() for item in siglist]
if args.containment:
similarity = compare_serial_containment(siglist)
else:
similarity = compare_all_pairs(siglist, args.ignore_abundance,
n_jobs=args.processes)
if len(siglist) < 30:
for i, E in enumerate(siglist):
# for small matrices, pretty-print some output
name_num = '{}-{}'.format(i, E.name())
if len(name_num) > 20:
name_num = name_num[:17] + '...'
print_results('{:20s}\t{}'.format(name_num, similarity[i, :, ],))
print_results('min similarity in matrix: {:.3f}', numpy.min(similarity))
# shall we output a matrix?
if args.output:
labeloutname = args.output + '.labels.txt'
notify('saving labels to: {}', labeloutname)
with open(labeloutname, 'w') as fp:
fp.write("\n".join(labeltext))
notify('saving comparison matrix to: {}', args.output)
with open(args.output, 'wb') as fp:
numpy.save(fp, similarity)
# output CSV?
if args.csv:
with FileOutput(args.csv, 'wt') as csv_fp:
w = csv.writer(csv_fp)
w.writerow(labeltext)
for i in range(len(labeltext)):
y = []
for j in range(len(labeltext)):
y.append('{}'.format(similarity[i][j]))
w.writerow(y)
def plot(args):
"Produce a clustering matrix and plot."
import matplotlib as mpl
mpl.use('Agg')
import numpy
import pylab
import scipy.cluster.hierarchy as sch
from . import fig as sourmash_fig
# load files
D_filename = args.distances
labelfilename = D_filename + '.labels.txt'
notify('loading comparison matrix from {}...', D_filename)
D = numpy.load(open(D_filename, 'rb'))
notify('...got {} x {} matrix.', *D.shape)
if args.labeltext:
labelfilename = args.labeltext
notify('loading labels from {}', labelfilename)
labeltext = [ x.strip() for x in open(labelfilename) ]
if len(labeltext) != D.shape[0]:
error('{} labels != matrix size, exiting', len(labeltext))
sys.exit(-1)
# build filenames, decide on PDF/PNG output
dendrogram_out = os.path.basename(D_filename) + '.dendro'
if args.pdf:
dendrogram_out += '.pdf'
else:
dendrogram_out += '.png'
matrix_out = os.path.basename(D_filename) + '.matrix'
if args.pdf:
matrix_out += '.pdf'
else:
matrix_out += '.png'
hist_out = os.path.basename(D_filename) + '.hist'
if args.pdf:
hist_out += '.pdf'
else:
hist_out += '.png'
# output to a different directory?
if args.output_dir:
if not os.path.isdir(args.output_dir):
os.mkdir(args.output_dir)
dendrogram_out = os.path.join(args.output_dir, dendrogram_out)
matrix_out = os.path.join(args.output_dir, matrix_out)
hist_out = os.path.join(args.output_dir, hist_out)
# make the histogram
notify('saving histogram of matrix values => {}', hist_out)
fig = pylab.figure(figsize=(8,5))
pylab.hist(numpy.array(D.flat), bins=100)
fig.savefig(hist_out)
### make the dendrogram:
fig = pylab.figure(figsize=(8,5))
ax1 = fig.add_axes([0.1, 0.1, 0.7, 0.8])
ax1.set_xticks([])
ax1.set_yticks([])
# subsample?
if args.subsample:
numpy.random.seed(args.subsample_seed)
sample_idx = list(range(len(labeltext)))
numpy.random.shuffle(sample_idx)
sample_idx = sample_idx[:args.subsample]
np_idx = numpy.array(sample_idx)
D = D[numpy.ix_(np_idx, np_idx)]
labeltext = [ labeltext[idx] for idx in sample_idx ]
### do clustering
Y = sch.linkage(D, method='single')
sch.dendrogram(Y, orientation='right', labels=labeltext)
fig.savefig(dendrogram_out)
notify('wrote dendrogram to: {}', dendrogram_out)
### make the dendrogram+matrix:
(fig, rlabels, rmat) = sourmash_fig.plot_composite_matrix(D, labeltext,
show_labels=args.labels,
show_indices=args.indices,
vmin=args.vmin,
vmax=args.vmax,
force=args.force)
fig.savefig(matrix_out)
notify('wrote numpy distance matrix to: {}', matrix_out)
if len(labeltext) < 30:
# for small matrices, print out sample numbering for FYI.
for i, name in enumerate(labeltext):
print_results('{}\t{}', i, name)
# write out re-ordered matrix and labels
if args.csv:
with FileOutput(args.csv, 'wt') as csv_fp:
w = csv.writer(csv_fp)
w.writerow(rlabels)
for i in range(len(rlabels)):
y = []
for j in range(len(rlabels)):
y.append('{}'.format(rmat[i][j]))
w.writerow(y)
notify('Wrote clustered matrix and labels out to {}', args.csv)
def import_csv(args):
"Import a CSV file full of signatures/hashes."
with open(args.mash_csvfile, 'r') as fp:
reader = csv.reader(fp)
siglist = []
for row in reader:
hashfn = row[0]
hashseed = int(row[1])
# only support a limited import type, for now ;)
assert hashfn == 'murmur64'
assert hashseed == 42
_, _, ksize, name, hashes = row
ksize = int(ksize)
hashes = hashes.strip()
hashes = list(map(int, hashes.split(' ' )))
e = MinHash(len(hashes), ksize)
e.add_many(hashes)
s = sig.SourmashSignature(e, filename=name)
siglist.append(s)
notify('loaded signature: {} {}', name, s.md5sum()[:8])
notify('saving {} signatures to JSON', len(siglist))
with FileOutput(args.output, 'wt') as outfp:
sig.save_signatures(siglist, outfp)
def sbt_combine(args):
inp_files = list(args.sbts)
notify('combining {} SBTs', len(inp_files))
tree = load_sbt_index(inp_files.pop(0))
for f in inp_files:
new_tree = load_sbt_index(f)
# TODO: check if parameters are the same for both trees!
tree.combine(new_tree)
notify('saving SBT under "{}".', args.sbt_name)
tree.save(args.sbt_name)
def index(args):
"""
Build a Sequence Bloom Tree index of the given signatures.
"""
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
if args.append:
tree = load_sbt_index(args.sbt_name)
else:
tree = create_sbt_index(args.bf_size, n_children=args.n_children)
if args.sparseness < 0 or args.sparseness > 1.0:
error('sparseness must be in range [0.0, 1.0].')
if args.scaled:
args.scaled = int(args.scaled)
notify('downsampling signatures to scaled={}', args.scaled)
inp_files = list(args.signatures)
if args.from_file:
more_files = sourmash_args.load_file_list_of_signatures(args.from_file)
inp_files.extend(more_files)
if not inp_files:
error("ERROR: no files to index!? Supply on command line or use --from-file")
sys.exit(-1)
notify('loading {} files into SBT', len(inp_files))
progress = sourmash_args.SignatureLoadingProgress()
n = 0
ksizes = set()
moltypes = set()
nums = set()
scaleds = set()
for f in inp_files:
siglist = sourmash_args.load_file_as_signatures(f,
ksize=args.ksize,
select_moltype=moltype,
yield_all_files=args.force,
progress=progress)
# load all matching signatures in this file
ss = None
for ss in siglist:
ksizes.add(ss.minhash.ksize)
moltypes.add(sourmash_args.get_moltype(ss))
nums.add(ss.minhash.num)
if args.scaled:
ss.minhash = ss.minhash.downsample(scaled=args.scaled)
scaleds.add(ss.minhash.scaled)
tree.insert(ss)
n += 1
if not ss:
continue
# check to make sure we aren't loading incompatible signatures
if len(ksizes) > 1 or len(moltypes) > 1:
error('multiple k-mer sizes or molecule types present; fail.')
error('specify --dna/--protein and --ksize as necessary')
error('ksizes: {}; moltypes: {}',
", ".join(map(str, ksizes)), ", ".join(moltypes))
sys.exit(-1)
if nums == { 0 } and len(scaleds) == 1:
pass # good
elif scaleds == { 0 } and len(nums) == 1:
pass # also good
else:
error('trying to build an SBT with incompatible signatures.')
error('nums = {}; scaleds = {}', repr(nums), repr(scaleds))
sys.exit(-1)
notify('')
# did we load any!?
if n == 0:
error('no signatures found to load into tree!? failing.')
sys.exit(-1)
notify('loaded {} sigs; saving SBT under "{}"', n, args.sbt_name)
tree.save(args.sbt_name, sparseness=args.sparseness)
def search(args):
from .search import search_databases
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
# set up the query.
query = sourmash_args.load_query_signature(args.query,
ksize=args.ksize,
select_moltype=moltype,
select_md5=args.md5)
notify('loaded query: {}... (k={}, {})', query.name()[:30],
query.minhash.ksize,
sourmash_args.get_moltype(query))
# downsample if requested
if args.scaled:
if query.minhash.max_hash == 0:
error('cannot downsample a signature not created with --scaled')
sys.exit(-1)
if args.scaled != query.minhash.scaled:
notify('downsampling query from scaled={} to {}',
query.minhash.scaled, int(args.scaled))
query.minhash = query.minhash.downsample(scaled=args.scaled)
# set up the search databases
databases = sourmash_args.load_dbs_and_sigs(args.databases, query,
not args.containment)
# forcibly ignore abundances if query has no abundances
if not query.minhash.track_abundance:
args.ignore_abundance = True
if not len(databases):
error('Nothing found to search!')
sys.exit(-1)
# do the actual search
results = search_databases(query, databases,
args.threshold, args.containment,
args.best_only, args.ignore_abundance,
unload_data=True)
n_matches = len(results)
if args.best_only:
args.num_results = 1
if not args.num_results or n_matches <= args.num_results:
print_results('{} matches:'.format(len(results)))
else:
print_results('{} matches; showing first {}:',
len(results), args.num_results)
n_matches = args.num_results
# output!
print_results("similarity match")
print_results("---------- -----")
for sr in results[:n_matches]:
pct = '{:.1f}%'.format(sr.similarity*100)
name = sr.match._display_name(60)
print_results('{:>6} {}', pct, name)
if args.best_only:
notify("** reporting only one match because --best-only was set")
if args.output:
fieldnames = ['similarity', 'name', 'filename', 'md5']
with FileOutput(args.output, 'wt') as fp:
w = csv.DictWriter(fp, fieldnames=fieldnames)
w.writeheader()
for sr in results:
d = dict(sr._asdict())
del d['match']
w.writerow(d)
# save matching signatures upon request
if args.save_matches:
notify('saving all matched signatures to "{}"', args.save_matches)
with FileOutput(args.save_matches, 'wt') as fp:
sig.save_signatures([ sr.match for sr in results ], fp)
def categorize(args):
"Use a database to find the best match to many signatures."
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
# eliminate names we've already categorized
already_names = set()
if args.load_csv:
with open(args.load_csv, 'rt') as fp:
r = csv.reader(fp)
for row in r:
already_names.add(row[0])
# load search database
tree = load_sbt_index(args.sbt_name)
# load query filenames
inp_files = set(sourmash_args.traverse_find_sigs(args.queries))
inp_files = inp_files - already_names
notify('found {} files to query', len(inp_files))
loader = sourmash_args.LoadSingleSignatures(inp_files,
args.ksize, moltype)
csv_w = None
csv_fp = None
if args.csv:
csv_fp = open(args.csv, 'wt')
csv_w = csv.writer(csv_fp)
for queryfile, query, query_moltype, query_ksize in loader:
notify('loaded query: {}... (k={}, {})', query.name()[:30],
query_ksize, query_moltype)
results = []
search_fn = SearchMinHashesFindBest().search
# note, "ignore self" here may prevent using newer 'tree.search' fn.
for leaf in tree.find(search_fn, query, args.threshold):
if leaf.data.md5sum() != query.md5sum(): # ignore self.
similarity = query.similarity(
leaf.data, ignore_abundance=args.ignore_abundance)
results.append((similarity, leaf.data))
best_hit_sim = 0.0
best_hit_query_name = ""
if results:
results.sort(key=lambda x: -x[0]) # reverse sort on similarity
best_hit_sim, best_hit_query = results[0]
notify('for {}, found: {:.2f} {}', query.name(),
best_hit_sim,
best_hit_query.name())
best_hit_query_name = best_hit_query.name()
else:
notify('for {}, no match found', query.name())
if csv_w:
csv_w.writerow([queryfile, query.name(), best_hit_query_name,
best_hit_sim])
if loader.skipped_ignore:
notify('skipped/ignore: {}', loader.skipped_ignore)
if loader.skipped_nosig:
notify('skipped/nosig: {}', loader.skipped_nosig)
if csv_fp:
csv_fp.close()
def gather(args):
from .search import gather_databases, format_bp
set_quiet(args.quiet, args.debug)
moltype = sourmash_args.calculate_moltype(args)
# load the query signature & figure out all the things
query = sourmash_args.load_query_signature(args.query,
ksize=args.ksize,
select_moltype=moltype,
select_md5=args.md5)
notify('loaded query: {}... (k={}, {})', query.name()[:30],
query.minhash.ksize,
sourmash_args.get_moltype(query))
# verify signature was computed right.
if query.minhash.scaled == 0:
error('query signature needs to be created with --scaled')
sys.exit(-1)
# downsample if requested
if args.scaled:
notify('downsampling query from scaled={} to {}',
query.minhash.scaled, int(args.scaled))
query.minhash = query.minhash.downsample(scaled=args.scaled)
# empty?
if not len(query.minhash):
error('no query hashes!? exiting.')
sys.exit(-1)
# set up the search databases
cache_size = args.cache_size
if args.cache_size == 0:
cache_size = None
databases = sourmash_args.load_dbs_and_sigs(args.databases, query, False,
cache_size=cache_size)
if not len(databases):
error('Nothing found to search!')
sys.exit(-1)
found = []
weighted_missed = 1
new_max_hash = query.minhash.max_hash
next_query = query
for result, weighted_missed, new_max_hash, next_query in gather_databases(query, databases, args.threshold_bp, args.ignore_abundance):
if not len(found): # first result? print header.
if query.minhash.track_abundance and not args.ignore_abundance:
print_results("")
print_results("overlap p_query p_match avg_abund")
print_results("--------- ------- ------- ---------")
else:
print_results("")
print_results("overlap p_query p_match")
print_results("--------- ------- -------")
# print interim result & save in a list for later use
pct_query = '{:.1f}%'.format(result.f_unique_weighted*100)
pct_genome = '{:.1f}%'.format(result.f_match*100)
average_abund ='{:.1f}'.format(result.average_abund)
name = result.match._display_name(40)
if query.minhash.track_abundance and not args.ignore_abundance:
print_results('{:9} {:>7} {:>7} {:>9} {}',
format_bp(result.intersect_bp), pct_query, pct_genome,
average_abund, name)
else:
print_results('{:9} {:>7} {:>7} {}',
format_bp(result.intersect_bp), pct_query, pct_genome,
name)
found.append(result)
if args.num_results and len(found) >= args.num_results:
break
# basic reporting
print_results('\nfound {} matches total;', len(found))
if args.num_results and len(found) == args.num_results:
print_results('(truncated gather because --num-results={})',
args.num_results)
print_results('the recovered matches hit {:.1f}% of the query',
(1 - weighted_missed) * 100)
print_results('')
if found and args.output:
fieldnames = ['intersect_bp', 'f_orig_query', 'f_match',
'f_unique_to_query', 'f_unique_weighted',
'average_abund', 'median_abund', 'std_abund', 'name',
'filename', 'md5', 'f_match_orig']
with FileOutput(args.output, 'wt') as fp:
w = csv.DictWriter(fp, fieldnames=fieldnames)
w.writeheader()
for result in found:
d = dict(result._asdict())
del d['match'] # actual signature not in CSV.
w.writerow(d)
if found and args.save_matches:
notify('saving all matches to "{}"', args.save_matches)
with FileOutput(args.save_matches, 'wt') as fp:
sig.save_signatures([ r.match for r in found ], fp)
if args.output_unassigned:
if not len(next_query.minhash):
notify('no unassigned hashes to save with --output-unassigned!')
else:
notify('saving unassigned hashes to "{}"', args.output_unassigned)
with FileOutput(args.output_unassigned, 'wt') as fp:
sig.save_signatures([ next_query ], fp)
def multigather(args):
"Gather many signatures against multiple databases."
from .search import gather_databases, format_bp
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
if not args.db:
error('Error! must specify at least one database with --db')
sys.exit(-1)
if not args.query and not args.query_from_file:
error('Error! must specify at least one query signature with --query')
sys.exit(-1)
# flatten --db and --query
args.db = [item for sublist in args.db for item in sublist]
inp_files = [item for sublist in args.query for item in sublist]
if args.query_from_file:
more_files = sourmash_args.load_file_list_of_signatures(args.query_from_file)
inp_files.extend(more_files)
# need a query to get ksize, moltype for db loading
query = next(iter(sourmash_args.load_file_as_signatures(inp_files[0], ksize=args.ksize, select_moltype=moltype)))
databases = sourmash_args.load_dbs_and_sigs(args.db, query, False)
if not len(databases):
error('Nothing found to search!')
sys.exit(-1)
# run gather on all the queries.
n=0
for queryfile in inp_files:
# load the query signature(s) & figure out all the things
for query in sourmash_args.load_file_as_signatures(queryfile,
ksize=args.ksize,
select_moltype=moltype):
notify('loaded query: {}... (k={}, {})', query.name()[:30],
query.minhash.ksize,
sourmash_args.get_moltype(query))
# verify signature was computed right.
if query.minhash.max_hash == 0:
error('query signature needs to be created with --scaled; skipping')
continue
# downsample if requested
if args.scaled:
notify('downsampling query from scaled={} to {}',
query.minhash.scaled, int(args.scaled))
query.minhash = query.minhash.downsample(scaled=args.scaled)
# empty?
if not len(query.minhash):
error('no query hashes!? skipping to next..')
continue
found = []
weighted_missed = 1
for result, weighted_missed, new_max_hash, next_query in gather_databases(query, databases, args.threshold_bp, args.ignore_abundance):
if not len(found): # first result? print header.
if query.minhash.track_abundance and not args.ignore_abundance:
print_results("")
print_results("overlap p_query p_match avg_abund")
print_results("--------- ------- ------- ---------")
else:
print_results("")
print_results("overlap p_query p_match")
print_results("--------- ------- -------")
# print interim result & save in a list for later use
pct_query = '{:.1f}%'.format(result.f_unique_weighted*100)
pct_genome = '{:.1f}%'.format(result.f_match*100)
average_abund ='{:.1f}'.format(result.average_abund)
name = result.match._display_name(40)
if query.minhash.track_abundance and not args.ignore_abundance:
print_results('{:9} {:>7} {:>7} {:>9} {}',
format_bp(result.intersect_bp), pct_query, pct_genome,
average_abund, name)
else:
print_results('{:9} {:>7} {:>7} {}',
format_bp(result.intersect_bp), pct_query, pct_genome,
name)
found.append(result)
# basic reporting
print_results('\nfound {} matches total;', len(found))
print_results('the recovered matches hit {:.1f}% of the query',
(1 - weighted_missed) * 100)
print_results('')
if not found:
notify('nothing found... skipping.')
continue
query_filename = query.filename
if not query_filename:
# use md5sum if query.filename not properly set
query_filename = query.md5sum()
output_base = os.path.basename(query_filename)
output_csv = output_base + '.csv'
fieldnames = ['intersect_bp', 'f_orig_query', 'f_match',
'f_unique_to_query', 'f_unique_weighted',
'average_abund', 'median_abund', 'std_abund', 'name',
'filename', 'md5', 'f_match_orig']
with open(output_csv, 'wt') as fp:
w = csv.DictWriter(fp, fieldnames=fieldnames)
w.writeheader()
for result in found:
d = dict(result._asdict())
del d['match'] # actual signature not output to CSV!
w.writerow(d)
output_matches = output_base + '.matches.sig'
with open(output_matches, 'wt') as fp:
outname = output_matches
notify('saving all matches to "{}"', outname)
sig.save_signatures([ r.match for r in found ], fp)
output_unassigned = output_base + '.unassigned.sig'
with open(output_unassigned, 'wt') as fp:
if not found:
notify('nothing found - entire query signature unassigned.')
elif not len(query.minhash):
notify('no unassigned hashes! not saving.')
else:
notify('saving unassigned hashes to "{}"', output_unassigned)
e = MinHash(ksize=query.minhash.ksize, n=0, max_hash=new_max_hash)
e.add_many(next_query.minhash.hashes)
sig.save_signatures([ sig.SourmashSignature(e) ], fp)
n += 1
# fini, next query!
notify('\nconducted gather searches on {} signatures', n)
def watch(args):
"Build a signature from raw FASTA/FASTQ coming in on stdin, search."
set_quiet(args.quiet)
if args.input_is_protein and args.dna:
notify('WARNING: input is protein, turning off nucleotide hashing.')
args.dna = False
args.protein = True
if args.dna and args.protein:
notify('ERROR: cannot use "watch" with both nucleotide and protein.')
if args.dna:
moltype = 'DNA'
is_protein = False
dayhoff = False
hp = False
elif args.protein:
moltype = 'protein'
is_protein = True
dayhoff = False
hp = False
elif args.dayhoff:
moltype = 'dayhoff'
is_protein = True
dayhoff = True
hp = False
else:
moltype = 'hp'
is_protein = True
dayhoff = False
hp = True
tree = load_sbt_index(args.sbt_name)
# check ksize from the SBT we are loading
ksize = args.ksize
if ksize is None:
leaf = next(iter(tree.leaves()))
tree_mh = leaf.data.minhash
ksize = tree_mh.ksize
E = MinHash(ksize=ksize, n=args.num_hashes, is_protein=is_protein, dayhoff=dayhoff, hp=hp)
notify('Computing signature for k={}, {} from stdin', ksize, moltype)
def do_search():
results = []
streamsig = sig.SourmashSignature(E, filename='stdin', name=args.name)
for similarity, match, _ in tree.search(streamsig,
threshold=args.threshold,
best_only=True,
ignore_abundance=True,
do_containment=False):
results.append((similarity, match))
return results
notify('reading sequences from stdin')
screed_iter = screed.open(args.inp_file)
watermark = WATERMARK_SIZE
# iterate over input records
n = 0
for n, record in enumerate(screed_iter):
# at each watermark, print status & check cardinality
if n >= watermark:
notify('\r... read {} sequences', n, end='')
watermark += WATERMARK_SIZE
if do_search():
break
if args.input_is_protein:
E.add_protein(record.sequence)
else:
E.add_sequence(record.sequence, False)
results = do_search()
if not results:
notify('... read {} sequences, no matches found.', n)
else:
results.sort(key=lambda x: -x[0]) # take best
similarity, found_sig = results[0]
print_results('FOUND: {}, at {:.3f}', found_sig.name(),
similarity)
if args.output:
notify('saving signature to {}', args.output)
with FileOutput(args.output, 'wt') as fp:
streamsig = sig.SourmashSignature(E, filename='stdin',
name=args.name)
sig.save_signatures([streamsig], fp)
def migrate(args):
"Migrate an SBT database to the latest version."
tree = load_sbt_index(args.sbt_name, print_version_warning=False)
notify('saving SBT under "{}".', args.sbt_name)
tree.save(args.sbt_name, structure_only=True)